Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations50000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 MiB
Average record size in memory248.0 B

Variable types

Numeric21
DateTime1
Categorical8
Text1

Alerts

avg_stars_top_3_players is highly overall correlated with playtime and 2 other fieldsHigh correlation
days_active_first_28_days_after_registration is highly overall correlated with playtimeHigh correlation
money_stash is highly overall correlated with rests_stashHigh correlation
morale_spent is highly overall correlated with playtime and 2 other fieldsHigh correlation
playtime is highly overall correlated with avg_stars_top_3_players and 8 other fieldsHigh correlation
registration_device_type is highly overall correlated with registration_platform_specific and 1 other fieldsHigh correlation
registration_platform_specific is highly overall correlated with registration_device_type and 1 other fieldsHigh correlation
registration_store is highly overall correlated with registration_device_type and 1 other fieldsHigh correlation
rests_spent is highly overall correlated with morale_spent and 3 other fieldsHigh correlation
rests_stash is highly overall correlated with money_stashHigh correlation
session_count is highly overall correlated with playtimeHigh correlation
tokens_bought is highly overall correlated with transaction_count_iapHigh correlation
tokens_spent is highly overall correlated with avg_stars_top_3_players and 4 other fieldsHigh correlation
tokens_stash is highly overall correlated with tokens_spentHigh correlation
total_match_played_count is highly overall correlated with total_match_watched_countHigh correlation
total_match_watched_count is highly overall correlated with playtime and 1 other fieldsHigh correlation
training_count is highly overall correlated with avg_stars_top_3_players and 5 other fieldsHigh correlation
transaction_count_iap is highly overall correlated with tokens_boughtHigh correlation
transaction_count_rewarded_video is highly overall correlated with playtime and 1 other fieldsHigh correlation
registration_platform_specific is highly imbalanced (52.8%) Imbalance
registration_store is highly imbalanced (61.9%) Imbalance
registration_device_type is highly imbalanced (68.3%) Imbalance
number_of_devices_used is highly imbalanced (95.2%) Imbalance
transaction_count_iap is highly skewed (γ1 = 22.60004975) Skewed
tokens_spent is highly skewed (γ1 = 52.9815726) Skewed
tokens_bought is highly skewed (γ1 = 167.0483388) Skewed
user_id has unique values Unique
total_match_played_count has 20918 (41.8%) zeros Zeros
total_match_watched_count has 39284 (78.6%) zeros Zeros
transaction_count_iap has 49445 (98.9%) zeros Zeros
transaction_count_rewarded_video has 34493 (69.0%) zeros Zeros
tokens_spent has 20335 (40.7%) zeros Zeros
tokens_stash has 965 (1.9%) zeros Zeros
tokens_bought has 49700 (99.4%) zeros Zeros
rests_spent has 31205 (62.4%) zeros Zeros
morale_spent has 33901 (67.8%) zeros Zeros
training_count has 18588 (37.2%) zeros Zeros
days_active_first_28_days_after_registration has 17491 (35.0%) zeros Zeros
transaction_count_iap_lifetime has 43825 (87.6%) zeros Zeros

Reproduction

Analysis started2024-11-17 15:33:34.276425
Analysis finished2024-11-17 15:37:14.333873
Duration3 minutes and 40.06 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

user_id
Real number (ℝ)

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109844.24
Minimum5
Maximum218956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:14.645387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11092.85
Q155070.75
median109571
Q3165091.75
95-th percentile208097.4
Maximum218956
Range218951
Interquartile range (IQR)110021

Descriptive statistics

Standard deviation63219.004
Coefficient of variation (CV)0.57553318
Kurtosis-1.2037434
Mean109844.24
Median Absolute Deviation (MAD)55047.5
Skewness-0.0066246035
Sum5.492212 × 109
Variance3.9966425 × 109
MonotonicityStrictly increasing
2024-11-17T16:37:14.962651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1
 
< 0.1%
147047 1
 
< 0.1%
146983 1
 
< 0.1%
146991 1
 
< 0.1%
146993 1
 
< 0.1%
146994 1
 
< 0.1%
147005 1
 
< 0.1%
147006 1
 
< 0.1%
147009 1
 
< 0.1%
147010 1
 
< 0.1%
Other values (49990) 49990
> 99.9%
ValueCountFrequency (%)
5 1
< 0.1%
7 1
< 0.1%
13 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
34 1
< 0.1%
35 1
< 0.1%
36 1
< 0.1%
39 1
< 0.1%
45 1
< 0.1%
ValueCountFrequency (%)
218956 1
< 0.1%
218949 1
< 0.1%
218948 1
< 0.1%
218947 1
< 0.1%
218943 1
< 0.1%
218939 1
< 0.1%
218932 1
< 0.1%
218925 1
< 0.1%
218924 1
< 0.1%
218923 1
< 0.1%
Distinct49410
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Minimum2024-05-19 00:00:59
Maximum2024-06-15 23:59:50
2024-11-17T16:37:15.417651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:37:16.054845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

registration_platform_specific
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Android Phone
34799 
iOS Phone
10895 
Android Tablet
 
1582
iOS Tablet
 
1309
UniversalWindows PC
 
755
Other values (2)
 
660

Length

Max length19
Median length13
Mean length12.1887
Min length9

Characters and Unicode

Total characters609435
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowiOS Phone
2nd rowAndroid Phone
3rd rowAndroid Phone
4th rowAndroid Phone
5th rowiOS Phone

Common Values

ValueCountFrequency (%)
Android Phone 34799
69.6%
iOS Phone 10895
 
21.8%
Android Tablet 1582
 
3.2%
iOS Tablet 1309
 
2.6%
UniversalWindows PC 755
 
1.5%
WebGL FB Canvas 415
 
0.8%
WebGL TE Site 245
 
0.5%

Length

2024-11-17T16:37:16.377895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T16:37:16.761966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
phone 45694
45.4%
android 36381
36.1%
ios 12204
 
12.1%
tablet 2891
 
2.9%
universalwindows 755
 
0.8%
pc 755
 
0.8%
webgl 660
 
0.7%
fb 415
 
0.4%
canvas 415
 
0.4%
te 245
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 84000
13.8%
o 82830
13.6%
d 73517
12.1%
50660
8.3%
i 50340
8.3%
e 50245
8.2%
P 46449
7.6%
h 45694
7.5%
r 37136
6.1%
A 36381
6.0%
Other values (18) 52183
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 442421
72.6%
Uppercase Letter 116354
 
19.1%
Space Separator 50660
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 84000
19.0%
o 82830
18.7%
d 73517
16.6%
i 50340
11.4%
e 50245
11.4%
h 45694
10.3%
r 37136
8.4%
a 4476
 
1.0%
l 3646
 
0.8%
b 3551
 
0.8%
Other values (4) 6986
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
P 46449
39.9%
A 36381
31.3%
S 12449
 
10.7%
O 12204
 
10.5%
T 3136
 
2.7%
W 1415
 
1.2%
C 1170
 
1.0%
U 755
 
0.6%
G 660
 
0.6%
L 660
 
0.6%
Other values (3) 1075
 
0.9%
Space Separator
ValueCountFrequency (%)
50660
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 558775
91.7%
Common 50660
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 84000
15.0%
o 82830
14.8%
d 73517
13.2%
i 50340
9.0%
e 50245
9.0%
P 46449
8.3%
h 45694
8.2%
r 37136
6.6%
A 36381
6.5%
S 12449
 
2.2%
Other values (17) 39734
7.1%
Common
ValueCountFrequency (%)
50660
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 84000
13.8%
o 82830
13.6%
d 73517
12.1%
50660
8.3%
i 50340
8.3%
e 50245
8.2%
P 46449
7.6%
h 45694
7.5%
r 37136
6.1%
A 36381
6.0%
Other values (18) 52183
8.6%

registration_store
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
GooglePlay
36017 
AppStore
12204 
WindowsStore
 
755
Facebook
 
660
Huawei
 
253
Other values (2)
 
111

Length

Max length15
Median length10
Mean length9.50578
Min length6

Characters and Unicode

Total characters475289
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAppStore
2nd rowGooglePlay
3rd rowGooglePlay
4th rowGooglePlay
5th rowAppStore

Common Values

ValueCountFrequency (%)
GooglePlay 36017
72.0%
AppStore 12204
 
24.4%
WindowsStore 755
 
1.5%
Facebook 660
 
1.3%
Huawei 253
 
0.5%
GooglePlayForPC 105
 
0.2%
Catappult 6
 
< 0.1%

Length

2024-11-17T16:37:17.525738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T16:37:18.270790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
googleplay 36017
72.0%
appstore 12204
 
24.4%
windowsstore 755
 
1.5%
facebook 660
 
1.3%
huawei 253
 
0.5%
googleplayforpc 105
 
0.2%
catappult 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 87383
18.4%
l 72250
15.2%
e 49994
10.5%
a 37047
7.8%
P 36227
7.6%
G 36122
7.6%
g 36122
7.6%
y 36122
7.6%
p 24420
 
5.1%
r 13064
 
2.7%
Other values (16) 46538
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 375893
79.1%
Uppercase Letter 99396
 
20.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 87383
23.2%
l 72250
19.2%
e 49994
13.3%
a 37047
9.9%
g 36122
9.6%
y 36122
9.6%
p 24420
 
6.5%
r 13064
 
3.5%
t 12971
 
3.5%
w 1008
 
0.3%
Other values (8) 5512
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
P 36227
36.4%
G 36122
36.3%
S 12959
 
13.0%
A 12204
 
12.3%
F 765
 
0.8%
W 755
 
0.8%
H 253
 
0.3%
C 111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 475289
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 87383
18.4%
l 72250
15.2%
e 49994
10.5%
a 37047
7.8%
P 36227
7.6%
G 36122
7.6%
g 36122
7.6%
y 36122
7.6%
p 24420
 
5.1%
r 13064
 
2.7%
Other values (16) 46538
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 87383
18.4%
l 72250
15.2%
e 49994
10.5%
a 37047
7.8%
P 36227
7.6%
G 36122
7.6%
g 36122
7.6%
y 36122
7.6%
p 24420
 
5.1%
r 13064
 
2.7%
Other values (16) 46538
9.8%

registration_season_day
Real number (ℝ)

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.61398
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:18.678593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q321
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.048579
Coefficient of variation (CV)0.59119956
Kurtosis-1.1781655
Mean13.61398
Median Absolute Deviation (MAD)7
Skewness0.13684414
Sum680699
Variance64.779624
MonotonicityNot monotonic
2024-11-17T16:37:19.211541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
8 2273
 
4.5%
1 2244
 
4.5%
5 2128
 
4.3%
7 2125
 
4.2%
4 1989
 
4.0%
15 1972
 
3.9%
2 1969
 
3.9%
3 1951
 
3.9%
6 1891
 
3.8%
14 1885
 
3.8%
Other values (18) 29573
59.1%
ValueCountFrequency (%)
1 2244
4.5%
2 1969
3.9%
3 1951
3.9%
4 1989
4.0%
5 2128
4.3%
6 1891
3.8%
7 2125
4.2%
8 2273
4.5%
9 1884
3.8%
10 1835
3.7%
ValueCountFrequency (%)
28 1608
3.2%
27 1476
3.0%
26 1382
2.8%
25 1444
2.9%
24 1524
3.0%
23 1572
3.1%
22 1851
3.7%
21 1728
3.5%
20 1635
3.3%
19 1555
3.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Paid
24678 
Organic
22745 
Unknown
2577 

Length

Max length7
Median length7
Mean length5.51932
Min length4

Characters and Unicode

Total characters275966
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrganic
2nd rowUnknown
3rd rowOrganic
4th rowPaid
5th rowOrganic

Common Values

ValueCountFrequency (%)
Paid 24678
49.4%
Organic 22745
45.5%
Unknown 2577
 
5.2%

Length

2024-11-17T16:37:19.583729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T16:37:19.866612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
paid 24678
49.4%
organic 22745
45.5%
unknown 2577
 
5.2%

Most occurring characters

ValueCountFrequency (%)
a 47423
17.2%
i 47423
17.2%
n 30476
11.0%
P 24678
8.9%
d 24678
8.9%
O 22745
8.2%
r 22745
8.2%
g 22745
8.2%
c 22745
8.2%
U 2577
 
0.9%
Other values (3) 7731
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 225966
81.9%
Uppercase Letter 50000
 
18.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 47423
21.0%
i 47423
21.0%
n 30476
13.5%
d 24678
10.9%
r 22745
10.1%
g 22745
10.1%
c 22745
10.1%
k 2577
 
1.1%
o 2577
 
1.1%
w 2577
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
P 24678
49.4%
O 22745
45.5%
U 2577
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 275966
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 47423
17.2%
i 47423
17.2%
n 30476
11.0%
P 24678
8.9%
d 24678
8.9%
O 22745
8.2%
r 22745
8.2%
g 22745
8.2%
c 22745
8.2%
U 2577
 
0.9%
Other values (3) 7731
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 275966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 47423
17.2%
i 47423
17.2%
n 30476
11.0%
P 24678
8.9%
d 24678
8.9%
O 22745
8.2%
r 22745
8.2%
g 22745
8.2%
c 22745
8.2%
U 2577
 
0.9%
Other values (3) 7731
 
2.8%

registration_device_type
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Phone
45694 
Tablet
 
2891
PC
 
1415

Length

Max length6
Median length5
Mean length4.97292
Min length2

Characters and Unicode

Total characters248646
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhone
2nd rowPhone
3rd rowPhone
4th rowPhone
5th rowPhone

Common Values

ValueCountFrequency (%)
Phone 45694
91.4%
Tablet 2891
 
5.8%
PC 1415
 
2.8%

Length

2024-11-17T16:37:20.410483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T16:37:20.747474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
phone 45694
91.4%
tablet 2891
 
5.8%
pc 1415
 
2.8%

Most occurring characters

ValueCountFrequency (%)
e 48585
19.5%
P 47109
18.9%
h 45694
18.4%
o 45694
18.4%
n 45694
18.4%
T 2891
 
1.2%
a 2891
 
1.2%
b 2891
 
1.2%
l 2891
 
1.2%
t 2891
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197231
79.3%
Uppercase Letter 51415
 
20.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48585
24.6%
h 45694
23.2%
o 45694
23.2%
n 45694
23.2%
a 2891
 
1.5%
b 2891
 
1.5%
l 2891
 
1.5%
t 2891
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
P 47109
91.6%
T 2891
 
5.6%
C 1415
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 248646
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48585
19.5%
P 47109
18.9%
h 45694
18.4%
o 45694
18.4%
n 45694
18.4%
T 2891
 
1.2%
a 2891
 
1.2%
b 2891
 
1.2%
l 2891
 
1.2%
t 2891
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 248646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 48585
19.5%
P 47109
18.9%
h 45694
18.4%
o 45694
18.4%
n 45694
18.4%
T 2891
 
1.2%
a 2891
 
1.2%
b 2891
 
1.2%
l 2891
 
1.2%
t 2891
 
1.2%
Distinct166
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:21.193716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length36
Median length26
Mean length6.12882
Min length2

Characters and Unicode

Total characters306441
Distinct characters59
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)0.2%

Sample

1st rowApple
2nd rowXiaomi
3rd rowXiaomi
4th rowOPPO
5th rowApple
ValueCountFrequency (%)
apple 12068
23.4%
samsung 11816
22.9%
xiaomi 9687
18.8%
oppo 3119
 
6.0%
vivo 2298
 
4.4%
realme 2200
 
4.3%
unknown 1813
 
3.5%
infinix 1467
 
2.8%
motorola 1464
 
2.8%
huawei 1139
 
2.2%
Other values (154) 4587
 
8.9%
2024-11-17T16:37:22.023144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 25583
 
8.3%
m 25190
 
8.2%
s 24231
 
7.9%
p 24167
 
7.9%
i 21920
 
7.2%
o 19154
 
6.3%
n 17795
 
5.8%
e 17534
 
5.7%
l 16665
 
5.4%
A 13309
 
4.3%
Other values (49) 100893
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 230876
75.3%
Uppercase Letter 73890
 
24.1%
Space Separator 1658
 
0.5%
Other Punctuation 8
 
< 0.1%
Decimal Number 5
 
< 0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 13309
18.0%
X 11198
15.2%
O 9148
12.4%
I 8608
11.6%
P 6511
8.8%
N 4141
 
5.6%
E 3120
 
4.2%
U 3011
 
4.1%
T 2214
 
3.0%
L 2053
 
2.8%
Other values (16) 10577
14.3%
Lowercase Letter
ValueCountFrequency (%)
a 25583
11.1%
m 25190
10.9%
s 24231
10.5%
p 24167
10.5%
i 21920
9.5%
o 19154
8.3%
n 17795
7.7%
e 17534
7.6%
l 16665
7.2%
u 12233
5.3%
Other values (15) 26404
11.4%
Decimal Number
ValueCountFrequency (%)
0 2
40.0%
2 1
20.0%
7 1
20.0%
3 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 7
87.5%
, 1
 
12.5%
Space Separator
ValueCountFrequency (%)
1658
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 304766
99.5%
Common 1675
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 25583
 
8.4%
m 25190
 
8.3%
s 24231
 
8.0%
p 24167
 
7.9%
i 21920
 
7.2%
o 19154
 
6.3%
n 17795
 
5.8%
e 17534
 
5.8%
l 16665
 
5.5%
A 13309
 
4.4%
Other values (41) 99218
32.6%
Common
ValueCountFrequency (%)
1658
99.0%
. 7
 
0.4%
- 4
 
0.2%
0 2
 
0.1%
2 1
 
0.1%
, 1
 
0.1%
7 1
 
0.1%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 25583
 
8.3%
m 25190
 
8.2%
s 24231
 
7.9%
p 24167
 
7.9%
i 21920
 
7.2%
o 19154
 
6.3%
n 17795
 
5.8%
e 17534
 
5.7%
l 16665
 
5.4%
A 13309
 
4.3%
Other values (49) 100893
32.9%

session_count
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.25444
Minimum0
Maximum52
Zeros240
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:22.413858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum52
Range52
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.107337
Coefficient of variation (CV)0.93474964
Kurtosis30.291855
Mean2.25444
Median Absolute Deviation (MAD)1
Skewness3.8362964
Sum112722
Variance4.4408691
MonotonicityNot monotonic
2024-11-17T16:37:22.855191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 24529
49.1%
2 11608
23.2%
3 5301
 
10.6%
4 3274
 
6.5%
5 1801
 
3.6%
6 1111
 
2.2%
7 689
 
1.4%
8 436
 
0.9%
9 301
 
0.6%
0 240
 
0.5%
Other values (24) 710
 
1.4%
ValueCountFrequency (%)
0 240
 
0.5%
1 24529
49.1%
2 11608
23.2%
3 5301
 
10.6%
4 3274
 
6.5%
5 1801
 
3.6%
6 1111
 
2.2%
7 689
 
1.4%
8 436
 
0.9%
9 301
 
0.6%
ValueCountFrequency (%)
52 1
 
< 0.1%
47 1
 
< 0.1%
38 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
26 3
< 0.1%
25 3
< 0.1%
24 7
< 0.1%

playtime
Real number (ℝ)

High correlation 

Distinct49357
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.315468
Minimum0
Maximum969.55977
Zeros240
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:23.188559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.84042
Q15.8783417
median19.197433
Q343.483663
95-th percentile107.45487
Maximum969.55977
Range969.55977
Interquartile range (IQR)37.605321

Descriptive statistics

Standard deviation40.880019
Coefficient of variation (CV)1.2650295
Kurtosis28.573793
Mean32.315468
Median Absolute Deviation (MAD)15.701283
Skewness3.5891723
Sum1615773.4
Variance1671.176
MonotonicityNot monotonic
2024-11-17T16:37:23.528615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 240
 
0.5%
6.081416667 3
 
< 0.1%
40.16773333 3
 
< 0.1%
1.967866667 3
 
< 0.1%
0.4678333333 2
 
< 0.1%
0.6544833333 2
 
< 0.1%
2.676383333 2
 
< 0.1%
0.4318833333 2
 
< 0.1%
4.649516667 2
 
< 0.1%
31.46425 2
 
< 0.1%
Other values (49347) 49739
99.5%
ValueCountFrequency (%)
0 240
0.5%
0.007133333333 1
 
< 0.1%
0.01176666667 1
 
< 0.1%
0.01351666667 1
 
< 0.1%
0.01846666667 1
 
< 0.1%
0.0191 1
 
< 0.1%
0.01953333333 1
 
< 0.1%
0.03146666667 1
 
< 0.1%
0.03665 1
 
< 0.1%
0.037 1
 
< 0.1%
ValueCountFrequency (%)
969.5597667 1
< 0.1%
902.6277167 1
< 0.1%
698.9254833 1
< 0.1%
673.3630333 1
< 0.1%
628.7657167 1
< 0.1%
616.14725 1
< 0.1%
579.4904833 1
< 0.1%
567.5388167 1
< 0.1%
534.24525 1
< 0.1%
526.6736667 1
< 0.1%

number_of_devices_used
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
1
49376 
2
 
369
0
 
240
3
 
14
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 49376
98.8%
2 369
 
0.7%
0 240
 
0.5%
3 14
 
< 0.1%
4 1
 
< 0.1%

Length

2024-11-17T16:37:23.820173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T16:37:24.091367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 49376
98.8%
2 369
 
0.7%
0 240
 
0.5%
3 14
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 49376
98.8%
2 369
 
0.7%
0 240
 
0.5%
3 14
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49376
98.8%
2 369
 
0.7%
0 240
 
0.5%
3 14
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 50000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49376
98.8%
2 369
 
0.7%
0 240
 
0.5%
3 14
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49376
98.8%
2 369
 
0.7%
0 240
 
0.5%
3 14
 
< 0.1%
4 1
 
< 0.1%

total_match_played_count
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.01376
Minimum0
Maximum29
Zeros20918
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:24.391216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum29
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6242978
Coefficient of variation (CV)1.6022509
Kurtosis30.090695
Mean1.01376
Median Absolute Deviation (MAD)1
Skewness4.3166377
Sum50688
Variance2.6383434
MonotonicityNot monotonic
2024-11-17T16:37:24.808887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 20918
41.8%
1 20427
40.9%
2 4962
 
9.9%
3 1161
 
2.3%
4 569
 
1.1%
5 494
 
1.0%
6 406
 
0.8%
7 331
 
0.7%
8 276
 
0.6%
9 141
 
0.3%
Other values (18) 315
 
0.6%
ValueCountFrequency (%)
0 20918
41.8%
1 20427
40.9%
2 4962
 
9.9%
3 1161
 
2.3%
4 569
 
1.1%
5 494
 
1.0%
6 406
 
0.8%
7 331
 
0.7%
8 276
 
0.6%
9 141
 
0.3%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 3
< 0.1%
21 5
< 0.1%
20 3
< 0.1%
19 5
< 0.1%
18 5
< 0.1%

total_match_watched_count
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35814
Minimum0
Maximum19
Zeros39284
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:25.227132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95195266
Coefficient of variation (CV)2.6580462
Kurtosis37.545697
Mean0.35814
Median Absolute Deviation (MAD)0
Skewness4.958619
Sum17907
Variance0.90621386
MonotonicityNot monotonic
2024-11-17T16:37:25.667978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 39284
78.6%
1 7438
 
14.9%
2 1685
 
3.4%
3 690
 
1.4%
4 321
 
0.6%
5 239
 
0.5%
6 134
 
0.3%
7 96
 
0.2%
8 44
 
0.1%
9 34
 
0.1%
Other values (7) 35
 
0.1%
ValueCountFrequency (%)
0 39284
78.6%
1 7438
 
14.9%
2 1685
 
3.4%
3 690
 
1.4%
4 321
 
0.6%
5 239
 
0.5%
6 134
 
0.3%
7 96
 
0.2%
8 44
 
0.1%
9 34
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
16 1
 
< 0.1%
15 3
 
< 0.1%
14 4
 
< 0.1%
13 5
 
< 0.1%
11 5
 
< 0.1%
10 16
 
< 0.1%
9 34
 
0.1%
8 44
0.1%
7 96
0.2%

transaction_count_iap
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0204
Minimum0
Maximum15
Zeros49445
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:26.101748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25216088
Coefficient of variation (CV)12.360828
Kurtosis808.14979
Mean0.0204
Median Absolute Deviation (MAD)0
Skewness22.60005
Sum1020
Variance0.063585112
MonotonicityNot monotonic
2024-11-17T16:37:26.447049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 49445
98.9%
1 331
 
0.7%
2 119
 
0.2%
3 50
 
0.1%
4 25
 
0.1%
5 13
 
< 0.1%
6 7
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 49445
98.9%
1 331
 
0.7%
2 119
 
0.2%
3 50
 
0.1%
4 25
 
0.1%
5 13
 
< 0.1%
6 7
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
9 2
 
< 0.1%
8 4
 
< 0.1%
7 2
 
< 0.1%
6 7
 
< 0.1%
5 13
 
< 0.1%
4 25
 
0.1%
3 50
 
0.1%
2 119
 
0.2%
1 331
0.7%

transaction_count_rewarded_video
Real number (ℝ)

High correlation  Zeros 

Distinct59
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.40398
Minimum0
Maximum58
Zeros34493
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:26.992503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum58
Range58
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.4706756
Coefficient of variation (CV)3.1842872
Kurtosis58.598554
Mean1.40398
Median Absolute Deviation (MAD)0
Skewness6.9188343
Sum70199
Variance19.98694
MonotonicityNot monotonic
2024-11-17T16:37:29.159230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34493
69.0%
1 5215
 
10.4%
2 2754
 
5.5%
3 1694
 
3.4%
4 1682
 
3.4%
5 1252
 
2.5%
6 743
 
1.5%
7 405
 
0.8%
8 297
 
0.6%
9 205
 
0.4%
Other values (49) 1260
 
2.5%
ValueCountFrequency (%)
0 34493
69.0%
1 5215
 
10.4%
2 2754
 
5.5%
3 1694
 
3.4%
4 1682
 
3.4%
5 1252
 
2.5%
6 743
 
1.5%
7 405
 
0.8%
8 297
 
0.6%
9 205
 
0.4%
ValueCountFrequency (%)
58 2
 
< 0.1%
57 1
 
< 0.1%
56 3
 
< 0.1%
55 4
 
< 0.1%
54 3
 
< 0.1%
53 4
 
< 0.1%
52 11
< 0.1%
51 7
< 0.1%
50 6
< 0.1%
49 11
< 0.1%

tokens_spent
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct398
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.42196
Minimum0
Maximum9182
Zeros20335
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:29.905635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q370
95-th percentile119
Maximum9182
Range9182
Interquartile range (IQR)70

Descriptive statistics

Standard deviation67.34237
Coefficient of variation (CV)1.8489497
Kurtosis6844.6676
Mean36.42196
Median Absolute Deviation (MAD)10
Skewness52.981573
Sum1821098
Variance4534.9948
MonotonicityNot monotonic
2024-11-17T16:37:30.525486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20335
40.7%
5 1065
 
2.1%
2 582
 
1.2%
1 510
 
1.0%
20 493
 
1.0%
80 471
 
0.9%
10 454
 
0.9%
6 421
 
0.8%
50 415
 
0.8%
83 413
 
0.8%
Other values (388) 24841
49.7%
ValueCountFrequency (%)
0 20335
40.7%
1 510
 
1.0%
2 582
 
1.2%
3 382
 
0.8%
4 369
 
0.7%
5 1065
 
2.1%
6 421
 
0.8%
7 365
 
0.7%
8 387
 
0.8%
9 338
 
0.7%
ValueCountFrequency (%)
9182 1
< 0.1%
1842 1
< 0.1%
1486 1
< 0.1%
1469 1
< 0.1%
1463 1
< 0.1%
1359 1
< 0.1%
1269 1
< 0.1%
1220 1
< 0.1%
1149 1
< 0.1%
1119 1
< 0.1%

tokens_stash
Real number (ℝ)

High correlation  Zeros 

Distinct789
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.74456
Minimum0
Maximum8261
Zeros965
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:30.892159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q129
median83
Q3113
95-th percentile211
Maximum8261
Range8261
Interquartile range (IQR)84

Descriptive statistics

Standard deviation120.16827
Coefficient of variation (CV)1.3242477
Kurtosis905.38124
Mean90.74456
Median Absolute Deviation (MAD)42
Skewness19.657883
Sum4537228
Variance14440.414
MonotonicityNot monotonic
2024-11-17T16:37:31.203826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 984
 
2.0%
0 965
 
1.9%
84 778
 
1.6%
2 771
 
1.5%
3 767
 
1.5%
83 763
 
1.5%
81 759
 
1.5%
85 745
 
1.5%
80 728
 
1.5%
87 723
 
1.4%
Other values (779) 42017
84.0%
ValueCountFrequency (%)
0 965
1.9%
1 984
2.0%
2 771
1.5%
3 767
1.5%
4 680
1.4%
5 593
1.2%
6 555
1.1%
7 489
1.0%
8 487
1.0%
9 473
0.9%
ValueCountFrequency (%)
8261 1
< 0.1%
6755 1
< 0.1%
6025 1
< 0.1%
5262 1
< 0.1%
4142 1
< 0.1%
3278 1
< 0.1%
3060 1
< 0.1%
3010 1
< 0.1%
2958 1
< 0.1%
2543 1
< 0.1%

tokens_bought
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7902
Minimum0
Maximum9000
Zeros49700
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:31.652482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9000
Range9000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation45.088538
Coefficient of variation (CV)57.059653
Kurtosis32186.54
Mean0.7902
Median Absolute Deviation (MAD)0
Skewness167.04834
Sum39510
Variance2032.9762
MonotonicityNot monotonic
2024-11-17T16:37:32.143126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49700
99.4%
20 145
 
0.3%
14 21
 
< 0.1%
80 21
 
< 0.1%
37 15
 
< 0.1%
176 14
 
< 0.1%
34 10
 
< 0.1%
100 7
 
< 0.1%
196 5
 
< 0.1%
28 4
 
< 0.1%
Other values (43) 58
 
0.1%
ValueCountFrequency (%)
0 49700
99.4%
14 21
 
< 0.1%
20 145
 
0.3%
25 1
 
< 0.1%
28 4
 
< 0.1%
34 10
 
< 0.1%
37 15
 
< 0.1%
45 1
 
< 0.1%
48 3
 
< 0.1%
51 1
 
< 0.1%
ValueCountFrequency (%)
9000 1
< 0.1%
3020 1
< 0.1%
1307 1
< 0.1%
1211 1
< 0.1%
1051 1
< 0.1%
1031 1
< 0.1%
852 2
< 0.1%
617 1
< 0.1%
537 1
< 0.1%
534 1
< 0.1%

rests_stash
Real number (ℝ)

High correlation 

Distinct1074
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.03252
Minimum0
Maximum1438
Zeros169
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:32.880183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q1148
median223
Q3381
95-th percentile915
Maximum1438
Range1438
Interquartile range (IQR)233

Descriptive statistics

Standard deviation237.83553
Coefficient of variation (CV)0.7771577
Kurtosis2.0560623
Mean306.03252
Median Absolute Deviation (MAD)87
Skewness1.5991885
Sum15301626
Variance56565.739
MonotonicityNot monotonic
2024-11-17T16:37:33.489421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1030 589
 
1.2%
145 412
 
0.8%
149 380
 
0.8%
147 374
 
0.7%
146 373
 
0.7%
148 364
 
0.7%
143 358
 
0.7%
141 354
 
0.7%
150 353
 
0.7%
153 349
 
0.7%
Other values (1064) 46094
92.2%
ValueCountFrequency (%)
0 169
0.3%
1 71
0.1%
2 52
 
0.1%
3 49
 
0.1%
4 46
 
0.1%
5 41
 
0.1%
6 52
 
0.1%
7 37
 
0.1%
8 35
 
0.1%
9 25
 
0.1%
ValueCountFrequency (%)
1438 1
< 0.1%
1163 1
< 0.1%
1124 1
< 0.1%
1099 1
< 0.1%
1085 1
< 0.1%
1084 1
< 0.1%
1072 1
< 0.1%
1071 1
< 0.1%
1068 1
< 0.1%
1067 1
< 0.1%

rests_spent
Real number (ℝ)

High correlation  Zeros 

Distinct434
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.80798
Minimum0
Maximum959
Zeros31205
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:34.020506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324
95-th percentile145
Maximum959
Range959
Interquartile range (IQR)24

Descriptive statistics

Standard deviation53.736718
Coefficient of variation (CV)2.1661061
Kurtosis22.395338
Mean24.80798
Median Absolute Deviation (MAD)0
Skewness3.7006928
Sum1240399
Variance2887.6349
MonotonicityNot monotonic
2024-11-17T16:37:34.589199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31205
62.4%
22 801
 
1.6%
1 561
 
1.1%
11 452
 
0.9%
2 448
 
0.9%
12 292
 
0.6%
157 288
 
0.6%
18 277
 
0.6%
24 256
 
0.5%
23 250
 
0.5%
Other values (424) 15170
30.3%
ValueCountFrequency (%)
0 31205
62.4%
1 561
 
1.1%
2 448
 
0.9%
3 224
 
0.4%
4 238
 
0.5%
5 131
 
0.3%
6 162
 
0.3%
7 115
 
0.2%
8 247
 
0.5%
9 157
 
0.3%
ValueCountFrequency (%)
959 1
< 0.1%
948 1
< 0.1%
815 1
< 0.1%
805 1
< 0.1%
799 1
< 0.1%
798 1
< 0.1%
737 1
< 0.1%
707 1
< 0.1%
644 1
< 0.1%
636 1
< 0.1%

morale_spent
Real number (ℝ)

High correlation  Zeros 

Distinct180
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.94448
Minimum0
Maximum361
Zeros33901
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:35.388405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile22
Maximum361
Range361
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.810084
Coefficient of variation (CV)2.7930306
Kurtosis71.791202
Mean4.94448
Median Absolute Deviation (MAD)0
Skewness6.863805
Sum247224
Variance190.71841
MonotonicityNot monotonic
2024-11-17T16:37:36.162992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33901
67.8%
10 1799
 
3.6%
9 1766
 
3.5%
8 1563
 
3.1%
7 1165
 
2.3%
11 1015
 
2.0%
6 1001
 
2.0%
5 688
 
1.4%
1 647
 
1.3%
4 555
 
1.1%
Other values (170) 5900
 
11.8%
ValueCountFrequency (%)
0 33901
67.8%
1 647
 
1.3%
2 430
 
0.9%
3 472
 
0.9%
4 555
 
1.1%
5 688
 
1.4%
6 1001
 
2.0%
7 1165
 
2.3%
8 1563
 
3.1%
9 1766
 
3.5%
ValueCountFrequency (%)
361 1
 
< 0.1%
292 1
 
< 0.1%
276 1
 
< 0.1%
256 1
 
< 0.1%
245 1
 
< 0.1%
240 1
 
< 0.1%
216 4
< 0.1%
211 1
 
< 0.1%
201 2
< 0.1%
200 1
 
< 0.1%

money_stash
Real number (ℝ)

High correlation 

Distinct48650
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2042835 × 108
Minimum0
Maximum2.2249259 × 109
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:36.622560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19539959
Q139930339
median1.7809436 × 108
Q38.2807859 × 108
95-th percentile1.2419809 × 109
Maximum2.2249259 × 109
Range2.2249259 × 109
Interquartile range (IQR)7.8814825 × 108

Descriptive statistics

Standard deviation4.4636556 × 108
Coefficient of variation (CV)1.0616923
Kurtosis-0.90046669
Mean4.2042835 × 108
Median Absolute Deviation (MAD)1.5709152 × 108
Skewness0.78434862
Sum2.1021418 × 1013
Variance1.9924222 × 1017
MonotonicityNot monotonic
2024-11-17T16:37:37.016955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
0.1%
20978179 8
 
< 0.1%
20976822 8
 
< 0.1%
79859286 8
 
< 0.1%
20977822 7
 
< 0.1%
20978215 7
 
< 0.1%
20977750 6
 
< 0.1%
79853929 6
 
< 0.1%
20978322 6
 
< 0.1%
20976715 6
 
< 0.1%
Other values (48640) 49911
99.8%
ValueCountFrequency (%)
0 27
0.1%
310863 1
 
< 0.1%
395577 1
 
< 0.1%
404640 1
 
< 0.1%
504408 1
 
< 0.1%
961300 1
 
< 0.1%
1959513 1
 
< 0.1%
2009832 1
 
< 0.1%
2366943 1
 
< 0.1%
2430790 1
 
< 0.1%
ValueCountFrequency (%)
2224925880 1
< 0.1%
2036765301 1
< 0.1%
1962456165 1
< 0.1%
1885093571 1
< 0.1%
1870749447 1
< 0.1%
1861381209 1
< 0.1%
1788311600 1
< 0.1%
1779771811 1
< 0.1%
1778082967 1
< 0.1%
1778008043 1
< 0.1%

avg_stars_top_3_players
Real number (ℝ)

High correlation 

Distinct48897
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6723753
Minimum0
Maximum8.2145777
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:37.521588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.58022
Q15.2968334
median5.7348222
Q36.0704
95-th percentile6.5330711
Maximum8.2145777
Range8.2145777
Interquartile range (IQR)0.77356661

Descriptive statistics

Standard deviation0.57528001
Coefficient of variation (CV)0.10141783
Kurtosis0.39354904
Mean5.6723753
Median Absolute Deviation (MAD)0.38135566
Skewness-0.34023398
Sum283618.77
Variance0.33094709
MonotonicityNot monotonic
2024-11-17T16:37:37.884258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.870933363 3
 
< 0.1%
5.886266615 3
 
< 0.1%
5.843822174 3
 
< 0.1%
5.442977787 3
 
< 0.1%
6.202800022 3
 
< 0.1%
6.007777778 3
 
< 0.1%
5.760444472 3
 
< 0.1%
5.402844374 3
 
< 0.1%
5.393733317 3
 
< 0.1%
5.597466634 3
 
< 0.1%
Other values (48887) 49970
99.9%
ValueCountFrequency (%)
0 3
< 0.1%
3.321466649 1
 
< 0.1%
3.384711109 1
 
< 0.1%
3.418266661 1
 
< 0.1%
3.451333364 1
 
< 0.1%
3.49564448 1
 
< 0.1%
3.519422184 1
 
< 0.1%
3.795066681 1
 
< 0.1%
3.808800049 1
 
< 0.1%
3.875155504 1
 
< 0.1%
ValueCountFrequency (%)
8.214577705 1
< 0.1%
7.946933272 1
< 0.1%
7.922222154 1
< 0.1%
7.912444594 1
< 0.1%
7.904933336 1
< 0.1%
7.894044478 1
< 0.1%
7.709066569 1
< 0.1%
7.699466824 1
< 0.1%
7.689733344 1
< 0.1%
7.653111131 1
< 0.1%

avg_age_top_11_players
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.771246
Minimum20
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:38.259805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21
Q122
median23
Q323
95-th percentile25
Maximum28
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0616579
Coefficient of variation (CV)0.046622739
Kurtosis0.17323706
Mean22.771246
Median Absolute Deviation (MAD)1
Skewness0.37049372
Sum1138562.3
Variance1.1271174
MonotonicityNot monotonic
2024-11-17T16:37:38.774763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
23 17554
35.1%
22 16209
32.4%
24 8623
17.2%
21 4657
 
9.3%
25 2282
 
4.6%
26 434
 
0.9%
20 194
 
0.4%
27 41
 
0.1%
22.77124627 3
 
< 0.1%
28 3
 
< 0.1%
ValueCountFrequency (%)
20 194
 
0.4%
21 4657
 
9.3%
22 16209
32.4%
22.77124627 3
 
< 0.1%
23 17554
35.1%
24 8623
17.2%
25 2282
 
4.6%
26 434
 
0.9%
27 41
 
0.1%
28 3
 
< 0.1%
ValueCountFrequency (%)
28 3
 
< 0.1%
27 41
 
0.1%
26 434
 
0.9%
25 2282
 
4.6%
24 8623
17.2%
23 17554
35.1%
22.77124627 3
 
< 0.1%
22 16209
32.4%
21 4657
 
9.3%
20 194
 
0.4%

training_count
Real number (ℝ)

High correlation  Zeros 

Distinct174
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.25404
Minimum0
Maximum516
Zeros18588
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:39.175648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile11
Maximum516
Range516
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.081785
Coefficient of variation (CV)3.4055467
Kurtosis556.26023
Mean3.25404
Median Absolute Deviation (MAD)1
Skewness19.681952
Sum162702
Variance122.80596
MonotonicityNot monotonic
2024-11-17T16:37:40.107390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18588
37.2%
1 8657
17.3%
2 5406
 
10.8%
3 4130
 
8.3%
4 3211
 
6.4%
5 2307
 
4.6%
6 1612
 
3.2%
7 1269
 
2.5%
8 961
 
1.9%
9 754
 
1.5%
Other values (164) 3105
 
6.2%
ValueCountFrequency (%)
0 18588
37.2%
1 8657
17.3%
2 5406
 
10.8%
3 4130
 
8.3%
4 3211
 
6.4%
5 2307
 
4.6%
6 1612
 
3.2%
7 1269
 
2.5%
8 961
 
1.9%
9 754
 
1.5%
ValueCountFrequency (%)
516 1
< 0.1%
463 1
< 0.1%
447 1
< 0.1%
424 1
< 0.1%
389 1
< 0.1%
383 1
< 0.1%
369 1
< 0.1%
367 1
< 0.1%
344 1
< 0.1%
336 1
< 0.1%

days_active_first_28_days_after_registration
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.44444
Minimum0
Maximum28
Zeros17491
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:40.592763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313
95-th percentile28
Maximum28
Range28
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.6981397
Coefficient of variation (CV)1.302736
Kurtosis-0.33329809
Mean7.44444
Median Absolute Deviation (MAD)2
Skewness1.1002734
Sum372222
Variance94.053914
MonotonicityNot monotonic
2024-11-17T16:37:40.861695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 17491
35.0%
1 5642
 
11.3%
28 3184
 
6.4%
2 3045
 
6.1%
3 2138
 
4.3%
4 1648
 
3.3%
5 1403
 
2.8%
27 1370
 
2.7%
6 1165
 
2.3%
7 1017
 
2.0%
Other values (19) 11897
23.8%
ValueCountFrequency (%)
0 17491
35.0%
1 5642
 
11.3%
2 3045
 
6.1%
3 2138
 
4.3%
4 1648
 
3.3%
5 1403
 
2.8%
6 1165
 
2.3%
7 1017
 
2.0%
8 836
 
1.7%
9 861
 
1.7%
ValueCountFrequency (%)
28 3184
6.4%
27 1370
2.7%
26 898
 
1.8%
25 762
 
1.5%
24 664
 
1.3%
23 573
 
1.1%
22 543
 
1.1%
21 531
 
1.1%
20 493
 
1.0%
19 541
 
1.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
43825 
1
6175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43825
87.6%
1 6175
 
12.3%

Length

2024-11-17T16:37:41.269026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T16:37:41.519250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43825
87.6%
1 6175
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 43825
87.6%
1 6175
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43825
87.6%
1 6175
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 50000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43825
87.6%
1 6175
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43825
87.6%
1 6175
 
12.3%

days_active_lifetime
Real number (ℝ)

Distinct521
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.53132
Minimum1
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:41.773635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median17
Q351
95-th percentile175
Maximum650
Range649
Interquartile range (IQR)47

Descriptive statistics

Standard deviation66.123853
Coefficient of variation (CV)1.5547096
Kurtosis12.398564
Mean42.53132
Median Absolute Deviation (MAD)15
Skewness3.0846103
Sum2126566
Variance4372.364
MonotonicityNot monotonic
2024-11-17T16:37:42.251124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3821
 
7.6%
1 3492
 
7.0%
3 3045
 
6.1%
4 2396
 
4.8%
5 1898
 
3.8%
6 1510
 
3.0%
7 1313
 
2.6%
8 1211
 
2.4%
9 987
 
2.0%
10 975
 
1.9%
Other values (511) 29352
58.7%
ValueCountFrequency (%)
1 3492
7.0%
2 3821
7.6%
3 3045
6.1%
4 2396
4.8%
5 1898
3.8%
6 1510
 
3.0%
7 1313
 
2.6%
8 1211
 
2.4%
9 987
 
2.0%
10 975
 
1.9%
ValueCountFrequency (%)
650 1
< 0.1%
626 1
< 0.1%
615 1
< 0.1%
611 1
< 0.1%
605 1
< 0.1%
604 1
< 0.1%
590 1
< 0.1%
586 1
< 0.1%
585 1
< 0.1%
580 1
< 0.1%

transaction_count_iap_lifetime
Real number (ℝ)

Zeros 

Distinct129
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.06164
Minimum0
Maximum335
Zeros43825
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2024-11-17T16:37:42.868015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum335
Range335
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.7633686
Coefficient of variation (CV)6.3706799
Kurtosis479.23783
Mean1.06164
Median Absolute Deviation (MAD)0
Skewness17.238154
Sum53082
Variance45.743155
MonotonicityNot monotonic
2024-11-17T16:37:43.359719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43825
87.6%
1 1828
 
3.7%
2 885
 
1.8%
3 608
 
1.2%
4 408
 
0.8%
5 332
 
0.7%
6 246
 
0.5%
7 180
 
0.4%
8 171
 
0.3%
9 144
 
0.3%
Other values (119) 1373
 
2.7%
ValueCountFrequency (%)
0 43825
87.6%
1 1828
 
3.7%
2 885
 
1.8%
3 608
 
1.2%
4 408
 
0.8%
5 332
 
0.7%
6 246
 
0.5%
7 180
 
0.4%
8 171
 
0.3%
9 144
 
0.3%
ValueCountFrequency (%)
335 1
< 0.1%
323 1
< 0.1%
225 1
< 0.1%
218 1
< 0.1%
206 1
< 0.1%
204 1
< 0.1%
199 1
< 0.1%
197 1
< 0.1%
196 1
< 0.1%
179 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
1
31622 
0
18378 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 31622
63.2%
0 18378
36.8%

Length

2024-11-17T16:37:43.875379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T16:37:44.238701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 31622
63.2%
0 18378
36.8%

Most occurring characters

ValueCountFrequency (%)
1 31622
63.2%
0 18378
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 31622
63.2%
0 18378
36.8%

Most occurring scripts

ValueCountFrequency (%)
Common 50000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 31622
63.2%
0 18378
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 31622
63.2%
0 18378
36.8%

continent
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Asia
23562 
Europe
16152 
South America
5328 
Africa
2672 
North America
 
2041
Other values (2)
 
245

Length

Max length13
Median length7
Mean length6.09408
Min length4

Characters and Unicode

Total characters304704
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEurope
2nd rowAsia
3rd rowEurope
4th rowAsia
5th rowAfrica

Common Values

ValueCountFrequency (%)
Asia 23562
47.1%
Europe 16152
32.3%
South America 5328
 
10.7%
Africa 2672
 
5.3%
North America 2041
 
4.1%
Oceania 241
 
0.5%
Unknown 4
 
< 0.1%

Length

2024-11-17T16:37:44.506233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T16:37:45.127374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
asia 23562
41.1%
europe 16152
28.2%
america 7369
 
12.8%
south 5328
 
9.3%
africa 2672
 
4.7%
north 2041
 
3.6%
oceania 241
 
0.4%
unknown 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 34085
11.2%
i 33844
11.1%
A 33603
11.0%
r 28234
9.3%
e 23762
7.8%
s 23562
7.7%
o 23525
7.7%
u 21480
7.0%
E 16152
 
5.3%
p 16152
 
5.3%
Other values (13) 50305
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 239966
78.8%
Uppercase Letter 57369
 
18.8%
Space Separator 7369
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 34085
14.2%
i 33844
14.1%
r 28234
11.8%
e 23762
9.9%
s 23562
9.8%
o 23525
9.8%
u 21480
9.0%
p 16152
6.7%
c 10282
 
4.3%
m 7369
 
3.1%
Other values (6) 17671
7.4%
Uppercase Letter
ValueCountFrequency (%)
A 33603
58.6%
E 16152
28.2%
S 5328
 
9.3%
N 2041
 
3.6%
O 241
 
0.4%
U 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7369
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 297335
97.6%
Common 7369
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 34085
11.5%
i 33844
11.4%
A 33603
11.3%
r 28234
9.5%
e 23762
8.0%
s 23562
7.9%
o 23525
7.9%
u 21480
7.2%
E 16152
 
5.4%
p 16152
 
5.4%
Other values (12) 42936
14.4%
Common
ValueCountFrequency (%)
7369
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 34085
11.2%
i 33844
11.1%
A 33603
11.0%
r 28234
9.3%
e 23762
7.8%
s 23562
7.7%
o 23525
7.7%
u 21480
7.0%
E 16152
 
5.3%
p 16152
 
5.3%
Other values (13) 50305
16.5%

Interactions

2024-11-17T16:36:56.893882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:33:54.540573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:02.023691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:14.286474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:22.584004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:31.206666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:40.962167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:50.718231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:59.331257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:09.439551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:18.033081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:24.618905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:33.097967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:42.048468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:51.642619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:59.823550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:07.344968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:14.037625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:22.756142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:31.603335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:41.505904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:57.697023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:33:54.889877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:02.422581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:14.574650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:22.932173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:31.521990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:41.312163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:51.007231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:59.789397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:09.861646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:18.311609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:24.900979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:33.488074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:42.363135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:51.949285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:00.209228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:07.621754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:14.320785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:23.066630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:31.905448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:42.156960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:58.479290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:33:55.217309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:03.592113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:14.891056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:23.214170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:31.918019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:41.720977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:51.296050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:00.428337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:10.815058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:18.606422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:25.193377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:33.859229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:42.702603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:52.391294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:00.538177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:07.909014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:14.642784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:23.411926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:32.307715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:43.248388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:59.228193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:33:55.598081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:05.209991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:15.199740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:23.497721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:32.407134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:42.056216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:51.580763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:00.841901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:11.671957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:18.897371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:25.471456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:34.230005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:43.163039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:52.844024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:00.879308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:08.249180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:14.963251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:23.782429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:32.691159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:44.430240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:37:00.931227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:33:55.978381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:05.627144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:15.500859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:23.780014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:32.809762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:42.399882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:51.890233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:01.142732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:12.325029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:19.253835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:25.746293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:34.576217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:43.510016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:53.166779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:01.307538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:08.582116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:15.349917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:24.112114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:33.030880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:46.180897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:37:02.459466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:33:56.294695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:06.013537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:15.951029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:24.088962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:33.210267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:42.753774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:52.189898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:01.447155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:12.760402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:19.595292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:26.106579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:34.915169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:44.235548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:53.592325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:01.736821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:08.895460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:15.815813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:24.495604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:33.436388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:47.641861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:37:03.271201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:33:56.662369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:06.588566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:16.485833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:24.393445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:33.556125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:43.253220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:52.600081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:01.856946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:13.174528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:19.899993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:26.437181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:35.286544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:44.943587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:53.957872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:02.257621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:09.210310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:16.300748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:25.034499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:33.868367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2024-11-17T16:35:23.023965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:30.508206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:40.118227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:48.891695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:57.814859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:05.691106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:12.447941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:20.625996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:29.010785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:37.250877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:54.954117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:37:09.532250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:00.684043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:12.920751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:21.259072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:28.824738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:38.956964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:48.736804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:56.517360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:06.933429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:16.617652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:23.335963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:31.000681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:40.511824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:49.616076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:58.225809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:06.103190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:12.752981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:20.988354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:29.520028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:37.712076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:55.394551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:37:09.854548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:00.998847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:13.266507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:21.564557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:29.254937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:39.327024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:49.300497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:57.491162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:07.415344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:16.970302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:23.639763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:31.502900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:40.961799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:50.058455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:58.547605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:06.418229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:13.094295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:21.293247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:30.009660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:38.195134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:55.783366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:37:10.177121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:01.361874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:13.665212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:21.948617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:29.829844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:39.700286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:49.850190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:57.988231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:08.305289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:17.405971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:24.032224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:31.870438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:41.289798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:50.484193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:58.886880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:06.713016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:13.446136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:21.821698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:30.465298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:39.001556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:56.094298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:37:10.535159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:01.675074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:13.968277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:22.237077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:30.280888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:40.427716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:50.361932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:34:58.631176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:09.110998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:17.719907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:24.316672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:32.237121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:41.623374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:51.123718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:35:59.305331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:07.019473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:13.737417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:22.190427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:31.262865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:39.938626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-11-17T16:36:56.421825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2024-11-17T16:37:45.786599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
avg_age_top_11_playersavg_stars_top_3_playerscontinentdays_active_first_28_days_after_registrationdays_active_lifetimeis_payer_lifetimeis_rewarded_video_watcher_lifetimemoney_stashmorale_spentnumber_of_devices_usedplaytimeregistration_channel_detailedregistration_device_typeregistration_platform_specificregistration_season_dayregistration_storerests_spentrests_stashsession_counttokens_boughttokens_spenttokens_stashtotal_match_played_counttotal_match_watched_counttraining_counttransaction_count_iaptransaction_count_iap_lifetimetransaction_count_rewarded_videouser_id
avg_age_top_11_players1.000-0.1490.009-0.088-0.0330.0130.035-0.022-0.0760.016-0.1320.0120.0100.0070.0070.006-0.086-0.005-0.075-0.010-0.1190.040-0.038-0.060-0.123-0.017-0.017-0.0920.000
avg_stars_top_3_players-0.1491.0000.0340.4200.2260.2010.2010.1920.3850.0650.6090.0580.0440.035-0.0020.0360.4450.0550.3520.0970.582-0.3100.2090.3070.5010.1450.1750.369-0.022
continent0.0090.0341.0000.0330.0250.0740.0950.0290.0030.0000.0280.1140.0730.1250.0170.1250.0040.0370.0240.0070.0000.0000.0220.0220.0070.0170.0070.0210.038
days_active_first_28_days_after_registration-0.0880.4200.0331.0000.3180.1030.2260.1930.3380.0360.5880.0510.0640.041-0.0230.0280.3760.0650.3810.0710.390-0.2130.1240.2880.4620.0980.1000.382-0.013
days_active_lifetime-0.0330.2260.0250.3181.0000.3160.3440.4180.1350.0140.1830.0650.0360.023-0.0100.0110.0900.2480.1210.0160.068-0.0400.0300.0800.1130.0260.3550.193-0.001
is_payer_lifetime0.0130.2010.0740.1030.3161.0000.2460.1590.0370.0170.0260.0450.0440.0910.0100.0810.0460.0990.0000.0280.0190.0090.0250.0180.0000.1010.2130.0770.108
is_rewarded_video_watcher_lifetime0.0350.2010.0950.2260.3440.2461.0000.2050.0330.0120.0750.0330.1290.1420.0150.1500.0570.0970.0690.0000.0040.0030.0600.0680.0290.0170.0570.1370.083
money_stash-0.0220.1920.0290.1930.4180.1590.2051.0000.0720.0410.0290.1480.0540.0590.0030.0490.0320.6780.0100.012-0.0670.249-0.025-0.0220.0280.0250.1730.123-0.073
morale_spent-0.0760.3850.0030.3380.1350.0370.0330.0721.0000.0000.5170.0050.0000.0000.0240.0000.754-0.1940.3210.0700.442-0.3080.2200.3200.5230.0920.0770.354-0.022
number_of_devices_used0.0160.0650.0000.0360.0140.0170.0120.0410.0001.0000.0370.1900.0520.0490.0120.0390.0230.0410.0700.0440.0330.0020.0330.0170.0000.0100.0050.0270.019
playtime-0.1320.6090.0280.5880.1830.0260.0750.0290.5170.0371.0000.0210.0440.040-0.0030.0370.630-0.1010.5650.0900.703-0.4170.3400.5420.7600.1160.0650.548-0.007
registration_channel_detailed0.0120.0580.1140.0510.0650.0450.0330.1480.0050.1900.0211.0000.1490.2020.0530.2060.0210.1010.0050.0000.0150.0300.0120.0140.0080.0060.0260.0280.098
registration_device_type0.0100.0440.0730.0640.0360.0440.1290.0540.0000.0520.0440.1491.0001.0000.0110.7250.0200.0370.0190.0000.0000.0000.0130.0110.0000.0040.0000.0300.076
registration_platform_specific0.0070.0350.1250.0410.0230.0910.1420.0590.0000.0490.0400.2021.0001.0000.0080.7150.0170.0510.0190.0040.0000.0100.0160.0130.0000.0170.0000.0220.071
registration_season_day0.007-0.0020.017-0.023-0.0100.0100.0150.0030.0240.012-0.0030.0530.0110.0081.0000.010-0.004-0.005-0.012-0.006-0.0410.037-0.093-0.0500.002-0.0040.012-0.024-0.007
registration_store0.0060.0360.1250.0280.0110.0810.1500.0490.0000.0390.0370.2060.7250.7150.0101.0000.0110.0470.0140.0000.0000.0060.0120.0110.0000.0140.0000.0210.063
rests_spent-0.0860.4450.0040.3760.0900.0460.0570.0320.7540.0230.6300.0210.0200.017-0.0040.0111.000-0.2640.3680.0840.552-0.3950.2680.3890.6720.1100.0580.401-0.010
rests_stash-0.0050.0550.0370.0650.2480.0990.0970.678-0.1940.041-0.1010.1010.0370.051-0.0050.047-0.2641.000-0.049-0.018-0.1480.426-0.085-0.111-0.151-0.0130.0840.010-0.076
session_count-0.0750.3520.0240.3810.1210.0000.0690.0100.3210.0700.5650.0050.0190.019-0.0120.0140.368-0.0491.0000.0630.371-0.1950.2950.3730.4120.0780.0110.335-0.030
tokens_bought-0.0100.0970.0070.0710.0160.0280.0000.0120.0700.0440.0900.0000.0000.004-0.0060.0000.084-0.0180.0631.0000.109-0.0470.0350.0480.0780.7350.1150.073-0.003
tokens_spent-0.1190.5820.0000.3900.0680.0190.004-0.0670.4420.0330.7030.0150.0000.000-0.0410.0000.552-0.1480.3710.1091.000-0.6860.2400.3500.5530.1310.0450.3220.002
tokens_stash0.040-0.3100.000-0.213-0.0400.0090.0030.249-0.3080.002-0.4170.0300.0000.0100.0370.006-0.3950.426-0.195-0.047-0.6861.000-0.158-0.236-0.323-0.0610.000-0.135-0.046
total_match_played_count-0.0380.2090.0220.1240.0300.0250.060-0.0250.2200.0330.3400.0120.0130.016-0.0930.0120.268-0.0850.2950.0350.240-0.1581.0000.5810.2420.0430.0090.1660.010
total_match_watched_count-0.0600.3070.0220.2880.0800.0180.068-0.0220.3200.0170.5420.0140.0110.013-0.0500.0110.389-0.1110.3730.0480.350-0.2360.5811.0000.3730.0670.0220.266-0.001
training_count-0.1230.5010.0070.4620.1130.0000.0290.0280.5230.0000.7600.0080.0000.0000.0020.0000.672-0.1510.4120.0780.553-0.3230.2420.3731.0000.1030.0590.545-0.015
transaction_count_iap-0.0170.1450.0170.0980.0260.1010.0170.0250.0920.0100.1160.0060.0040.017-0.0040.0140.110-0.0130.0780.7350.131-0.0610.0430.0670.1031.0000.1630.092-0.003
transaction_count_iap_lifetime-0.0170.1750.0070.1000.3550.2130.0570.1730.0770.0050.0650.0260.0000.0000.0120.0000.0580.0840.0110.1150.0450.0000.0090.0220.0590.1631.0000.1300.021
transaction_count_rewarded_video-0.0920.3690.0210.3820.1930.0770.1370.1230.3540.0270.5480.0280.0300.022-0.0240.0210.4010.0100.3350.0730.322-0.1350.1660.2660.5450.0920.1301.000-0.011
user_id0.000-0.0220.038-0.013-0.0010.1080.083-0.073-0.0220.019-0.0070.0980.0760.071-0.0070.063-0.010-0.076-0.030-0.0030.002-0.0460.010-0.001-0.015-0.0030.021-0.0111.000

Missing values

2024-11-17T16:37:11.172480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-17T16:37:13.380660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idregistration_time_utcregistration_platform_specificregistration_storeregistration_season_dayregistration_channel_detailedregistration_device_typeregistration_device_manufacturersession_countplaytimenumber_of_devices_usedtotal_match_played_counttotal_match_watched_counttransaction_count_iaptransaction_count_rewarded_videotokens_spenttokens_stashtokens_boughtrests_stashrests_spentmorale_spentmoney_stashavg_stars_top_3_playersavg_age_top_11_playerstraining_countdays_active_first_28_days_after_registrationis_payer_lifetimedays_active_lifetimetransaction_count_iap_lifetimeis_rewarded_video_watcher_lifetimecontinent
052024-05-25 01:26:48iOS PhoneAppStore7OrganicPhoneApple12.35103312000011405240010660767725.45151122.00001901Europe
172024-06-14 20:09:31Android PhoneGooglePlay27UnknownPhoneXiaomi18.69590010000015709620012244464295.30382222.01002401Asia
2132024-05-31 09:06:51Android PhoneGooglePlay13OrganicPhoneXiaomi228.386383100002023208290012217921425.74688924.030022201Europe
3232024-05-31 04:00:33Android PhoneGooglePlay13PaidPhoneOPPO453.0283331310079801803999281127096.69257822.0710201Asia
4252024-06-15 19:56:05iOS PhoneAppStore28OrganicPhoneApple597.78201711100777002098023265534985.77528924.06301901Africa
5342024-06-04 11:23:49iOS PhoneAppStore17PaidPhoneApple655.08008312102371180448223911823060696.02880023.025011101Europe
6352024-05-25 18:58:57Android PhoneGooglePlay7PaidPhonesamsung116.5135501000058705110012164707146.16133422.032613631South America
7362024-05-29 11:20:56Android PhoneGooglePlay11PaidPhonesamsung260.578217111203065213100824610936926286.31488822.0100100Europe
8392024-06-04 14:00:39Android PhoneGooglePlay17PaidPhonesamsung19.47950011000010504370013078012345.33377823.00005501Asia
9452024-05-25 20:05:22Android PhoneGooglePlay7PaidPhoneXiaomi119.66995015000503106770012142792856.39168923.0120401Europe
user_idregistration_time_utcregistration_platform_specificregistration_storeregistration_season_dayregistration_channel_detailedregistration_device_typeregistration_device_manufacturersession_countplaytimenumber_of_devices_usedtotal_match_played_counttotal_match_watched_counttransaction_count_iaptransaction_count_rewarded_videotokens_spenttokens_stashtokens_boughtrests_stashrests_spentmorale_spentmoney_stashavg_stars_top_3_playersavg_age_top_11_playerstraining_countdays_active_first_28_days_after_registrationis_payer_lifetimedays_active_lifetimetransaction_count_iap_lifetimeis_rewarded_video_watcher_lifetimecontinent
499902189232024-05-24 08:12:17iOS PhoneAppStore6OrganicPhoneApple120.7081501100174130203007249718386.35542222.02014321Asia
499912189242024-06-03 13:56:15WebGL FB CanvasFacebook16OrganicPCUnknown22.5389331100009507280011082587044.34942224.00003300Europe
499922189252024-05-22 12:30:42Android PhoneGooglePlay4PaidPhonesamsung22.2794001100008010300010530808834.83964522.0020013501Europe
499932189322024-05-21 15:57:06Android PhoneGooglePlay3OrganicPhonemotorola237.88023310000101130172163171688682546.12742225.010702001Europe
499942189392024-05-30 16:29:57Android PhoneGooglePlay12UnknownPhoneOPPO16.94601710000016205930013735529355.26488923.00019321Asia
499952189432024-05-26 13:45:01iOS PhoneAppStore8OrganicPhoneApple240.6686331100418180622103102583372686.03822224.07100900Europe
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